P689: CLASSIFICATION OF MDS AND AML BASED SOLELY ON GENETICS OMITTING BLAST COUNTING – A WAY TO RESOLVE THE DISCREPANCIES BETWEEN CLASSIFICATIONS ACCORDING TO WHO AND ICC?
Authors: Claudia Haferlach, Sandra Huber, Constance Baer, Stephan Hutter, Manja Meggendorfer, Wolfgang Kern, Torsten Haferlach, Gregor Hoermann
Journal: HemaSphere (2023)

Topic: 9. Myelodysplastic syndromes - Biology & Translational Research Background: The bone marrow blast percentage defining the boundary of MDS and AML within myeloid neoplasms (MN) has been reassessed several times in the past. Currently different blast count thresholds are used in the 5th edition of the WHO classification (WHO 2022) and the International Consensus Classification (ICC). Aims: In order to finally solve the problem and make blast counting obsolete, our aim was to propose a classification of MDS and AML that relies on genetic markers only. Methods: The cohort comprised 722 de novo MDS and 747 de novo AML. The diagnosis was established following WHO 2022. In all cases cytomorphology, chromosome banding analysis and WGS (100x, NovaSeq) were performed. Variants were called with Strelka2, Manta and GATK using a tumor w/o normal pipeline. For all cases survival data was available (median follow-up: MDS: 9.3 yrs, AML: 6.7 yrs). Results: First, we aimed to assign as many patients (pts) as possible into predefined biological/genetic subgroups irrespective of the blast cell count. 507/747 AML pts were assigned into one of the WHO classes “AML with defining genetic abnormalities (DGA)“. The remaining AML were combined with 718 MDS cases for further analyses. Based on genomic pattern analysis, pts were hierarchically assigned into 5 additional biological subgroups according to the presence of one of the following defining abnormalities: 1. deletion 5q (5q; n=217), 2. SF3B1 mutation (SF3B1; n=208), 3. mutations in other spliceosome genes (SRSF2, U2AF1, ZRSF2) (SP; n=266), 4. mutations in IDH1/2, DNMT3A, TET2 or ASXL1 (IDH/DTA; n=143), 5. no abnormality or other abnormalities (NO; n=124) (Figure – Genetics). Second, we introduced progression marker (PM) categories as an additional layer. Based on combined genomic and survival data, 5 progression marker (PM) patterns were identified and grouped into 3 categories: A. high risk PM defining the category of “acute MN”, i.e. a) biallelic TP53 alterations (TP53bi), b) mutations in RUNX1 or ASXL1and mutations in RAS pathway genes or KMT2A-PTD (R/A+RAS/PTD); B. other PM defining the category of “high risk MN”, i.e. c) complex karyotype without TP53bi (complex), d) RUNX1, or ASXL1 mutations (R/A), e) RAS pathway mutations or KMT2A-PTD (RAS/PTD); or C. the absence of these PM patterns defining “low risk MN” (Figure – Category). Overall 506 pts were classified as low risk MN, 286 as high risk MN, and 166 pts as acute MN based on the PM pattern. Median overall survival (OS) differed significantly within these categories (6.8 vs 1.9 vs 0.6 yrs, p<0.001). In addition, the presence of PM worsened outcome substantially in all biological/genetic subgroups (Figure). The frequency of PM patterns differed between the predefined biological/genetic subgroups: e.g. TP53bi (35%) and complex karyotype (12%) were the most frequent PM in 5q. Also the frequency of no PM was different within the subgroups (5q: 47%, SF3B1: 77%, SP: 27%, IDH/DTA: 44%, NO: 74%). In addition, the biological subgroup retained some relevance within the PM categories as shown by differences in median OS within low risk MN (range: 5.3 - 7.4 yrs), within high risk MN (range: 1.3 - 3.3 yrs), or within acute MN (range: 0.3 - 1.1 yrs) (Figure). Summary/Conclusion: AML and MDS can be classified into distinct biological subgroups and PM categories solely based on genetics omitting arbitrary blast count thresholds. Further, in AML and/or MDS without AML defining genetic abnormalities the continuum between MDS is reflected by the introduction of biological subgroups and PM. This proposal might pave a way to overcome the discrepancies between WHO 2022 and ICC.Keywords: MDS, AML